Dimensionality Reduction in Machine Learning Course Overview

Dimensionality Reduction in Machine Learning refers to the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It is a technique that allows for simplification of complex models and avoids the curse of dimensionality, thus enhancing the performance efficiency of machine learning models. The technique is utilized by industries to analyze and interpret multidimensional datasets, extract relevant information, eliminate redundancies and irrelevant data, thereby improving the model’s predictive performance. Techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), or Generalized Discriminant Analysis (GDA) are often used for dimensionality reduction.